import torch
import torch.nn as nn
import torch.nn.functional as F


###########################################################################################################
class SEModule(nn.Module):
    def __init__(self, channels, reduction=16):
        super(SEModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc_1 = nn.Conv2d(
            channels, channels // reduction, kernel_size=1, padding=0
        )
        self.relu = nn.ReLU(inplace=True)
        self.fc_2 = nn.Conv2d(
            channels // reduction, channels, kernel_size=1, padding=0
        )
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        original = x
        x = self.avg_pool(x)
        x = self.fc_1(x)
        x = self.relu(x)
        x = self.fc_2(x)
        x = self.sigmoid(x)
        return original * x


###########################################################################################################
class BasicConv(nn.Module):
    def __init__(
        self,
        in_planes,
        out_planes,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        relu=True,
        bn=True,
        bias=False,
    ):
        super(BasicConv, self).__init__()
        self.out_planes = out_planes
        self.conv = nn.Conv2d(
            in_planes,
            out_planes,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
        )
        self.bn = (
            nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True)
            if bn
            else None
        )
        self.relu = nn.ReLU() if relu else None

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.relu is not None:
            x = self.relu(x)
        return x


class Flatten(nn.Module):
    def forward(self, x):
        return x.view(x.size(0), -1)


class ChannelGate(nn.Module):
    def __init__(
        self, gate_channels, reduction_ratio=16, pool_types=["avg", "max"]
    ):
        super(ChannelGate, self).__init__()
        self.gate_channels = gate_channels
        self.mlp = nn.Sequential(
            Flatten(),
            nn.Linear(gate_channels, gate_channels // reduction_ratio),
            nn.ReLU(),
            nn.Linear(gate_channels // reduction_ratio, gate_channels),
        )
        self.pool_types = pool_types

    def forward(self, x):
        channel_att_sum = None
        for pool_type in self.pool_types:
            if pool_type == "avg":
                avg_pool = F.avg_pool2d(
                    x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3))
                )
                channel_att_raw = self.mlp(avg_pool)
            elif pool_type == "max":
                max_pool = F.max_pool2d(
                    x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3))
                )
                channel_att_raw = self.mlp(max_pool)
            elif pool_type == "lp":
                lp_pool = F.lp_pool2d(
                    x,
                    2,
                    (x.size(2), x.size(3)),
                    stride=(x.size(2), x.size * (3)),
                )
                channel_att_raw = self.mlp(lp_pool)
            elif pool_type == "lse":
                # LSE pool
                lse_pool = logsumexp_2d(x)
                channel_att_raw = self.mlp(lse_pool)

            if channel_att_sum is None:
                channel_att_sum = channel_att_raw
            else:
                channel_att_sum = channel_att_sum + channel_att_raw
        scale = (
            F.sigmoid(channel_att_sum).unsqueeze(2).unsqueeze(3).expand_as(x)
        )
        return x * scale


def logsumexp_2d(tensor):
    tensor_flatten = tensor.view(tensor.size(0), tensor.size(1), -1)
    s, _ = torch.max(tensor_flatten, dim=2, keepdim=True)
    outputs = s + (tensor_flatten - s).exp().sum(dim=2, keepdim=True).log()
    return outputs


class ChannelPool(nn.Module):
    def forward(self, x):
        return torch.cat(
            (torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1).unsqueeze(1)),
            dim=1,
        )


class SpatialGate(nn.Module):
    def __init__(self):
        super(SpatialGate, self).__init__()
        kernel_size = 7
        self.compress = ChannelPool()
        self.spatial = BasicConv(
            2,
            1,
            kernel_size,
            stride=1,
            padding=(kernel_size - 1) // 2,
            relu=False,
        )

    def forward(self, x):
        x_compress = self.compress(x)
        x_out = self.spatial(x_compress)
        scale = F.sigmoid(x_out)
        return x * scale


class CBAM(nn.Module):
    def __init__(
        self,
        gate_channels,
        reduction_ratio=16,
        pool_types=["avg", "max"],
        no_spatial=False,
    ):
        super(CBAM, self).__init__()
        self.ChannelGate = ChannelGate(
            gate_channels, reduction_ratio, pool_types
        )
        self.no_spatial = no_spatial
        if not no_spatial:
            self.SpatialGate = SpatialGate()

    def forward(self, x):
        x_out = self.ChannelGate(x)
        if not self.no_spatial:
            x_out = self.SpatialGate(x_out)
        return x_out
